# Natural Language Toolkit: Aligned Sentences
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Will Zhang <wilzzha@gmail.com>
# Guan Gui <ggui@student.unimelb.edu.au>
# Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from __future__ import print_function, unicode_literals
from nltk.compat import python_2_unicode_compatible, string_types
from nltk.metrics import precision, recall
import subprocess
@python_2_unicode_compatible
[docs]class AlignedSent(object):
"""
Return an aligned sentence object, which encapsulates two sentences
along with an ``Alignment`` between them.
>>> from nltk.align import AlignedSent
>>> algnsent = AlignedSent(['klein', 'ist', 'das', 'Haus'],
... ['the', 'house', 'is', 'small'], '0-2 1-3 2-1 3-0')
>>> algnsent.words
['klein', 'ist', 'das', 'Haus']
>>> algnsent.mots
['the', 'house', 'is', 'small']
>>> algnsent.alignment
Alignment([(0, 2), (1, 3), (2, 1), (3, 0)])
>>> algnsent.precision('0-2 1-3 2-1 3-3')
0.75
>>> from nltk.corpus import comtrans
>>> print(comtrans.aligned_sents()[54])
<AlignedSent: 'Weshalb also sollten...' -> 'So why should EU arm...'>
>>> print(comtrans.aligned_sents()[54].alignment)
0-0 0-1 1-0 2-2 3-4 3-5 4-7 5-8 6-3 7-9 8-9 9-10 9-11 10-12 11-6 12-6 13-13
:param words: source language words
:type words: list(str)
:param mots: target language words
:type mots: list(str)
:param alignment: the word-level alignments between the source
and target language
:type alignment: Alignment
"""
def __init__(self, words=[], mots=[], alignment='', encoding='utf8'):
self._words = words
self._mots = mots
self.alignment = alignment
@property
def words(self):
return self._words
@property
def mots(self):
return self._mots
def _get_alignment(self):
return self._alignment
def _set_alignment(self, alignment):
if not isinstance(alignment, Alignment):
alignment = Alignment(alignment)
self._check_align(alignment)
self._alignment = alignment
alignment = property(_get_alignment, _set_alignment)
def _check_align(self, a):
"""
Check whether the alignments are legal.
:param a: alignment to be checked
:raise IndexError: if alignment is out of sentence boundary
:rtype: boolean
"""
if not all(0 <= p[0] < len(self._words) for p in a):
raise IndexError("Alignment is outside boundary of words")
if not all(p[1] is None or 0 <= p[1] < len(self._mots) for p in a):
raise IndexError("Alignment is outside boundary of mots")
return True
def __repr__(self):
"""
Return a string representation for this ``AlignedSent``.
:rtype: str
"""
words = "[%s]" % (", ".join("'%s'" % w for w in self._words))
mots = "[%s]" % (", ".join("'%s'" % w for w in self._mots))
return "AlignedSent(%s, %s, %r)" % (words, mots, self._alignment)
def _to_dot(self):
"""
Dot representation of the aligned sentence
"""
s = 'graph align {\n'
s += 'node[shape=plaintext]\n'
# Declare node
for w in self._words:
s += '"%s_source" [label="%s"] \n' % (w, w)
for w in self._mots:
s += '"%s_target" [label="%s"] \n' % (w, w)
# Alignment
for u,v in self._alignment:
s += '"%s_source" -- "%s_target" \n' % (self._words[u] , self._mots[v] )
# Connect the source words
for i in range(len(self._words)-1) :
s += '"%s_source" -- "%s_source" [style=invis]\n' % (self._words[i] , self._words[i+1])
# Connect the target words
for i in range(len(self._mots)-1) :
s += '"%s_target" -- "%s_target" [style=invis]\n' % (self._mots[i] , self._mots[i+1])
# Put it in the same rank
s += '{rank = same; %s}\n' % (' '.join('"%s_source"' % w for w in self._words))
s += '{rank = same; %s}\n' % (' '.join('"%s_target"' % w for w in self._mots))
s += '}'
return s
def _repr_svg_(self):
"""
Ipython magic : show SVG representation of this ``AlignedSent``.
"""
dot_string = self._to_dot().encode('utf8')
output_format = 'svg'
try:
process = subprocess.Popen(['dot', '-T%s' % output_format], stdin=subprocess.PIPE,
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except OSError:
raise Exception('Cannot find the dot binary from Graphviz package')
out, err = process.communicate(dot_string)
return out
def __str__(self):
"""
Return a human-readable string representation for this ``AlignedSent``.
:rtype: str
"""
source = " ".join(self._words)[:20] + "..."
target = " ".join(self._mots)[:20] + "..."
return "<AlignedSent: '%s' -> '%s'>" % (source, target)
[docs] def invert(self):
"""
Return the aligned sentence pair, reversing the directionality
:rtype: AlignedSent
"""
return AlignedSent(self._mots, self._words,
self._alignment.invert())
[docs] def precision(self, reference):
"""
Return the precision of an aligned sentence with respect to a
"gold standard" reference ``AlignedSent``.
:type reference: AlignedSent or Alignment
:param reference: A "gold standard" reference aligned sentence.
:rtype: float or None
"""
# Get alignments in set of 2-tuples form
# The "possible" precision is used since it doesn't penalize for finding
# an alignment that was marked as "possible" (NAACL corpus)
align = self.alignment
if isinstance(reference, AlignedSent):
possible = reference.alignment
else:
possible = Alignment(reference)
return precision(possible, align)
[docs] def recall(self, reference):
"""
Return the recall of an aligned sentence with respect to a
"gold standard" reference ``AlignedSent``.
:type reference: AlignedSent or Alignment
:param reference: A "gold standard" reference aligned sentence.
:rtype: float or None
"""
# Get alignments in set of 2-tuples form
# The "sure" recall is used so we don't penalize for missing an
# alignment that was only marked as "possible".
align = self.alignment
if isinstance(reference, AlignedSent):
sure = reference.alignment
else:
sure = Alignment(reference)
# Call NLTKs existing functions for recall
return recall(sure, align)
[docs] def alignment_error_rate(self, reference, possible=None):
"""
Return the Alignment Error Rate (AER) of an aligned sentence
with respect to a "gold standard" reference ``AlignedSent``.
Return an error rate between 0.0 (perfect alignment) and 1.0 (no
alignment).
>>> from nltk.align import AlignedSent
>>> s = AlignedSent(["the", "cat"], ["le", "chat"], [(0, 0), (1, 1)])
>>> s.alignment_error_rate(s)
0.0
:type reference: AlignedSent or Alignment
:param reference: A "gold standard" reference aligned sentence.
:type possible: AlignedSent or Alignment or None
:param possible: A "gold standard" reference of possible alignments
(defaults to *reference* if None)
:rtype: float or None
"""
# Get alignments in set of 2-tuples form
align = self.alignment
if isinstance(reference, AlignedSent):
sure = reference.alignment
else:
sure = Alignment(reference)
if possible is not None:
# Set possible alignment
if isinstance(possible, AlignedSent):
possible = possible.alignment
else:
possible = Alignment(possible)
else:
# Possible alignment is just sure alignment
possible = sure
# Sanity check
assert(sure.issubset(possible))
# Return the Alignment Error Rate
return (1.0 - float(len(align & sure) + len(align & possible)) /
float(len(align) + len(sure)))
@python_2_unicode_compatible
[docs]class Alignment(frozenset):
"""
A storage class for representing alignment between two sequences, s1, s2.
In general, an alignment is a set of tuples of the form (i, j, ...)
representing an alignment between the i-th element of s1 and the
j-th element of s2. Tuples are extensible (they might contain
additional data, such as a boolean to indicate sure vs possible alignments).
>>> from nltk.align import Alignment
>>> a = Alignment([(0, 0), (0, 1), (1, 2), (2, 2)])
>>> a.invert()
Alignment([(0, 0), (1, 0), (2, 1), (2, 2)])
>>> print(a.invert())
0-0 1-0 2-1 2-2
>>> a[0]
[(0, 1), (0, 0)]
>>> a.invert()[2]
[(2, 1), (2, 2)]
>>> b = Alignment([(0, 0), (0, 1)])
>>> b.issubset(a)
True
>>> c = Alignment('0-0 0-1')
>>> b == c
True
"""
def __new__(cls, string_or_pairs):
if isinstance(string_or_pairs, string_types):
string_or_pairs = [_giza2pair(p) for p in string_or_pairs.split()]
self = frozenset.__new__(cls, string_or_pairs)
self._len = (max(p[0] for p in self) if self != frozenset([]) else 0)
self._index = None
return self
def __getitem__(self, key):
"""
Look up the alignments that map from a given index or slice.
"""
if not self._index:
self._build_index()
return self._index.__getitem__(key)
[docs] def invert(self):
"""
Return an Alignment object, being the inverted mapping.
"""
return Alignment(((p[1], p[0]) + p[2:]) for p in self)
[docs] def range(self, positions=None):
"""
Work out the range of the mapping from the given positions.
If no positions are specified, compute the range of the entire mapping.
"""
image = set()
if not self._index:
self._build_index()
if not positions:
positions = list(range(len(self._index)))
for p in positions:
image.update(f for _,f in self._index[p])
return sorted(image)
def __repr__(self):
"""
Produce a Giza-formatted string representing the alignment.
"""
return "Alignment(%r)" % sorted(self)
def __str__(self):
"""
Produce a Giza-formatted string representing the alignment.
"""
return " ".join("%d-%d" % p[:2] for p in sorted(self))
def _build_index(self):
"""
Build a list self._index such that self._index[i] is a list
of the alignments originating from word i.
"""
self._index = [[] for _ in range(self._len + 1)]
for p in self:
self._index[p[0]].append(p)
def _giza2pair(pair_string):
i, j = pair_string.split("-")
return int(i), int(j)
def _naacl2pair(pair_string):
i, j, p = pair_string.split("-")
return int(i), int(j)